Prior knowledge driven Granger causality analysis on gene regulatory network discovery

被引:0
作者
Shun Yao
Shinjae Yoo
Dantong Yu
机构
[1] Stony Brook University,Department of Biochemistry and Cell Biology
[2] Brookhaven National Laboratory,Computational Science Center
来源
BMC Bioinformatics | / 16卷
关键词
Time series; Gene expression data; Granger causality; Gene regulatory networks;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 147 条
[1]  
Mardis ER(2011)A decade’s perspective on DNA sequencing technology Nature 470 198-203
[2]  
Pop M(2008)Bioinformatics challenges of new sequencing technology Trends Genet 24 142-9
[3]  
Salzberg SL(2009)Gene regulatory network inference: Data integration in dynamic models—a review Biosystems 96 86-103
[4]  
Hecker M(2004)The ENCODE (ENCyclopedia of DNA elements) project Science 306 636-40
[5]  
Lambeck S(2013)NCBI GEO: archive for functional genomics data sets—update Nucleic Acids Res 41 991-5
[6]  
Toepfer S(2006)Inferring gene regulatory networks from time series data using the minimum description length principle Bioinformatics 22 2129-35
[7]  
van Someren E(2012)Boolean network inference from time series data incorporating prior biological knowledge BMC Genomics 13 9-65
[8]  
Guthke R(2004)Dynamic bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data Biosystems 75 57-9
[9]  
Feingold E(2005)A new dynamic bayesian network (dbn) approach for identifying gene regulatory networks from time course microarray data Bioinformatics 21 71-90
[10]  
Good P(2010)Characterizing dynamic changes in the human blood transcriptional network PLoS Comput. Biol 6 1000671-438